CLASS SELECTION METHODS FOR LAND COVER MAPPING WITHOUT REFERENCE DATA OF THE CORRESPONDING PERIOD

摘要

Many Earth monitoring applications use land cover maps, with increasing demands in terms of accuracy and short production delay. In this context, methods based on supervised classification of satellite image time series are often used because they allow to reach the required accuracy. However, they require a lot of reference data to be efficient. Previous works have shown that supervised classifiers trained with images and reference data of previous periods and followed by voting based fusion can achieve very good performances. However, voting approaches can lead to indecisions when there is too much disagreement between individual classifiers. This paper proposes to use frequent class transitions and the class history of each pixel to select the labels in case of tie during the voting step. Experimental results are obtained using a dataset of 7 years of image times series and reference data. The experiments show that the use of transition information notably improves the mapping accuracy.